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  • In our contribution, we present two different meth-ods for visualizing correlations between features. This can be very useful when we have no prior information about a classification of a data and we want to reduce number of features. The first method is correlation-based neural gas which is derived from the neural gas algorithm. The second method finds a global optima for the coordinates using a particle swarm optimization heuristice.
  • In our contribution, we present two different meth-ods for visualizing correlations between features. This can be very useful when we have no prior information about a classification of a data and we want to reduce number of features. The first method is correlation-based neural gas which is derived from the neural gas algorithm. The second method finds a global optima for the coordinates using a particle swarm optimization heuristice. (en)
Title
  • Visualizing Correlations Between EEG Features by Two Different Methods
  • Visualizing Correlations Between EEG Features by Two Different Methods (en)
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  • Visualizing Correlations Between EEG Features by Two Different Methods
  • Visualizing Correlations Between EEG Features by Two Different Methods (en)
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  • RIV/68407700:21230/12:00199167!RIV13-MSM-21230___
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  • S, Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat
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  • 177590
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  • RIV/68407700:21230/12:00199167
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  • EEG Features; Neural Gas; Particle Swarm Opti-Mization; Correlation (en)
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http://linked.open...ontrolniKodProRIV
  • [017900820F60]
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  • Praha
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  • Praha
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  • BioDat 2012 - Conference on Advanced Methods of Biological Data and Signal Processing
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  • Štěpánová, Karla
  • Macaš, Martin
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number of pages
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  • České vysoké učení technické v Praze
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  • 978-80-01-05153-5
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  • 21230
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